The decoder in a U-shaped segmentation network is used to receive the feature maps sent by the encoder, and the segmentation performance refines step by step as the features in the decoder decrease in the number of channels and increase in spatial resolution. However, as the network grows deep, the deepest decoder blocks become hard to train, which could limit the final segmentation performance. Deep supervision strategies have been proposed to train deep convolutional networks[1]}[2]}. In the proposed Prior Attention Network, the auxiliary predictions are extracted from different levels of decoder blocks and supervised with the same ground truth.
